LTX2.3 Long-Form MV: A For-Loop That Turns Six Images and One Song Into a Minute of Video

Generating a music video longer than a single LTX2.3 window means looping, and looping means solving the boring parts: slicing the audio per segment, walking image and prompt indices, and stitching without a visible cut. This episode's graph does all of that in one 133-node workflow. The author reports a one-minute result from six images and one song. Evidence note: one workflow JSON ships with the episode; its node graph and sampler settings are quoted below. Timing and capability claims are the author's, as reported in the video. When to use it You have a song and a set of consistent character images and want a music video, not a clip. You want per-shot prompts and per-shot durations rather than one long prompt. You can accept segment-level stitching instead of true single-pass generation. What is different in this workflow For-loop over segments: each loop walks an image index, a prompt index and an audio window; the author's slicing expression is b * a (segment seconds x index). Audio is cut, not repeated: every pass renders 0-10s, 10-20s, 20-30s of the song. Two prompt layers: a batch prompt list (one line per shot, six preset) feeding Prompt Relay, plus a global prompt. Four render passes: base generation, 2x latent upscale, subtitle/watermark removal, final 2x upscale. The author says this will be simplified to three. Motion control LoRA is wired in, so camera instructions in the global prompt actually bind. Step by step Load images: one image = one loop, three images = three loops. Per-image shot length is set below (the author uses 10 s). Load the song with LoadAudio; the loop only renders as much as the audio allows. Fill the batch prompt list, one line per shot; fill the global prompt for the whole piece. To add shots, copy-paste an image input block and reconnect - indices increment automatically. Run. Pass 1 generates, pass 2 doubles resolution, pass 3 strips burned subtitles, pass 4 doubles again. If identity drifts, tighten the camera wording in the global prompt; the motion-control branch is always connected, so camera instructions always apply. Parameter basis Graph: For循环Ltx2.3长MV自动生成工作流.json, 133 nodes. Checkpoint: CheckpointLoaderSimple -> ltx-2.3-22b-dev-dare-ties-distilled-1.1.safetensors. LoRAs: ltx-2.3-22b-ic-lora-motion-track-control-ref0.5.safetensors (1.0), LTX2.3_Crisp_Enhance.safetensors (1.0), Ltx2.3-Licon-VBVR-I2V-390K-R32.safetensors (1.0). Samplers: KSamplerSelect values euler_ancestral_cfg_pp, euler_cfg_pp, euler_ancestral; CFGGuider CFG 1 on all four; SamplerCustomAdvanced with ManualSigmas lists, e.g. 1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0 for the base pass and 0.85, 0.7250, 0.4219, 0.0 for the refinement passes. Frame rate: 24 fps (author states 24 in the video). Timing: per-shot 10 s; 6 shots ~ 1 minute. Latent handling: LTXVConcatAVLatent / LTXVSeparateAVLatent per pass, SetLatentNoiseMask and SolidMask for the masked passes, LTXVLatentUpsampler for the doubles. Limits Segment stitching is not free: the author tested a 21:9 input and still calls identity slightly reduced - camera control is the countermeasure. No first/last-frame loop: the author says LTX2.3's first/last loop is less smooth than his Wan2.1 version; no post-hoc video extension either, because it decays. Longer than ~1 minute is a waiting game even with the loop. Four render passes are heavy; the author himself calls the graph bloated. Shot images were made with GPT Image 2 ("G2.0") in the demo; any consistent character images work. Episode resources RunningHub - for-loop LTX2.3 long MV: https://www.runninghub.ai/post/2055888057307549697/?inviteCode=rh-v1111 YouTube: https://www.youtube.com/watch?v=eqTjKOURnF0 Bilibili: https://www.bilibili.com/video/BV1CSLJ6vEMQ